Thymol is a monoterpene phenolic derivative extracted from the Thymus vulgaris which has antimicrobial effects. In the present study, thymol-loaded chitosan nanogels were prepared and their physicochemical properties were characterized. The encapsulation efficiency of thymol into chitosan and its stability were determined. The in vitro antimicrobial and anti-biofilm activities of thymol-loaded chitosan nanogel (Ty-CsNG), free thymol (Ty), and free chitosan nanogel (CsNG) were evaluated against both Gram-negative and Grampositive multidrug-resistant (MDR) bacteria including Staphylococcus aureus, Acinetobacter baumanii, and Pseudomonas aeruginosa strains using the broth microdilution and crystal violet assay, respectively. After treatment of MDR strains with sub-minimum inhibitory concentration (Sub-MIC) of Ty-CsNG, free Ty and CsNG, biofilm gene expression analysis was studied. Moreover, cytotoxicity of Ty-CsNG, free Ty, and CsNG against HEK-293 normal cell line was determined using MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) method. The average size of Ty-CsNG was 82.71 � 9.6 nm, encapsulation efficiency was 76.54 � 0.62 % with stability up to 60 days at 4 °C. Antibacterial activity test revealed that Ty-CsNG reduced the MIC by 4 -6 times in comparison to free thymol. In addition, the expression of biofilm-related genes including ompA, and pgaB were significantly down-regulated after treatment of strains with Ty-CsNG (P < 0.05). In addition, free CsNG displayed negligible cytotoxicity against HEK-293 normal cell lines and presented a biocompatible nanoscale delivery system. Based on the results, it can be concluded that Ty-CsNG can be considered a promising candidate for enhancing antimicrobial and anti-biofilm activities.

BackgroundEsophageal squamous cell carcinoma (ESCC) is the most common histological type of esophageal cancer, with a poor prognosis. Deregulation of WNT and NOTCH signaling pathways is important in ESCC progression, which can be due to either malfunction of their components or crosstalk with other pathways. Therefore, identification of new crosstalk between such pathways may be effective to introduce new strategies for targeted therapy of cancer. A correlation study was performed to assess the probable interaction between growth factor receptors and WNT/NOTCH pathways via the epidermal growth factor receptor (EGFR) and Musashi1 (MSI1), respectively.MethodsLevels of MSI1/EGFR mRNA expression in tumor tissues from 48 ESCC patients were compared to their corresponding normal tissues using real-time polymerase chain reaction.ResultsThere was a significant correlation between EGFR and MSI1 expression (p = 0.05). Moreover, there was a significant correlation between EGFR/MSI1 expression and grade of tumor differentiation (p = 0.02).ConclusionThis study confirms a direct correlation between MSI1 and EGFR and may support the important role of MSI1 in activation of EGFR through NOTCH/WNT pathways in ESCC.

We aim to assess the antibacterial and anti-biofilm properties of Niosome-encapsulated Imipenem. After isolating Staphylococcus epidermidis isolates and determining their microbial sensitivity, their ability to form biofilms was examined using plate microtiter assay. Various formulations of Niosome-encapsulated Imipenem were prepared using the thin-film hydration method, Minimum Biofilm Inhibitory Concentration (MBIC) and Minimum Inhibitory Concentration (MIC) were determined, and biofilm genes expression was examined. Drug formulations’ toxicity effect on HDF cells were determined using MTT assay. Out of the 162 separated S. epidermidis, 106 were resistant to methicillin. 87 MRSE isolates were vancomycin-resistant, all of which could form biofilms. The F1 formulation of niosomal Imipenem with a size of 192.3 ± 5.84 and an encapsulation index of 79.36 ± 1.14 was detected, which prevented biofilm growth with a BGI index of 69% and reduced icaD, FnbA, EbpS biofilms’ expression with P ≤ 0.001 in addition to reducing MBIC and MIC by 4–6 times. Interestingly, F1 formulation of niosomal Imipenem indicated cell viability over 90% at all tested concentrations. The results of the present study indicate that Niosome-encapsulated Imipenem reduces the resistance of MRSE to antibiotics in addition to increasing its anti-biofilm and antibiotic activity, and could prove useful as a new strategy for drug delivery.

The coronavirus outbreak continues to spread around the world and no one knows when it will stop. Therefore, from the first day of the identification of the virus in Wuhan, China, scientists have launched numerous research projects to understand the nature of the virus, how to detect it, and search for the most effective medicine to help and protect patients. Importantly, a rapid diagnostic and detection system is a priority and should be developed to stop COVID-19 from spreading. Medical imaging techniques have been used for this purpose. Current research is focused on exploiting different backbones like VGG, ResNet, DenseNet, or combine them to detect COVID-19. By using these backbones many aspects cannot be analyzed like the spatial and contextual information in the images, although this information can be useful for more robust detection performance. In this paper, we used 3D representation of the data as input for the proposed 3DCNN-based deep learning model. The process includes using the Bi-dimensional Empirical Mode Decomposition (BEMD) technique to decompose the original image into IMFs, and then building a video of these IMF images. The formed video is used as input for the 3DCNN model to classify and detect the COVID-19 virus. The 3DCNN model consists of a 3D VGG-16 backbone followed by a Context-aware attention (CAA) module, and then fully connected layers for classification. Each CAA module takes the feature maps of different blocks of the backbone, which allows learning from different feature maps. In our experiments, we used 6484 X-ray images, of which 1802 were COVID-19 positive cases, 1910 normal cases, and 2772 pneumonia cases. The experiment results showed that our proposed technique achieved the desired results on the selected dataset. Additionally, the use of the 3DCNN model with contextual information processing exploited CAA networks to achieve better performance.

Background and Objective: Since health information is one of the most important needs of human in each society, foreign immigrants face oftenwith problems for safe, reliable and fast information. The aim of this study is to investigate of health information needs and barriers to access among foreign immigrants in Iran. Materials and Methods: The survey-analytical study was carried among 384 Iraqi and Afghan legal immigrants who are living in 8 provinces of Iran. We used questionnaires (which designed in Persian and Arabic languages) and SPSS software to collect and analysis data. Result: Findings showed that there are no significant difference between health information needs of level of education and History of illness. Also "General Health Information" (4.20) mentioned as the most important health information needs for immigrants and "Family, Friends and other immigrants" (3.83) were the most important source of access to information for them. Conclusion: Health knowledge and information of foreign immigrants in Iran were in low status. Recognition and supply of their health information, removing the barriers and improve their health literacy should be considered by Health policy makers.

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Coronavirus outbreak continues to spread around the world and none knows when it will stop. Therefore, from the first day of the virus identification in Wuhan, scientists have launched numerous research projects to understand the nature of the virus, how to detect it, and search for the right medicine to help and protect patients. A fast diagnostic and detection system is a priority and should be found to stop COVID-19 from spreading. Medical imaging techniques has been used for this purpose. The existing works used transfer learning by exploiting different backbones like VGG, ResNet, DenseNet or combine them to detect COVID-19. By using these backbones many aspect can not be analysed like the spatial and contextual information in the images, while these information's can be useful for a better detection performance. For that in this paper, we used 3D representation of the data (video) as input of the 3DCNN-based deep learning model. The Bi-dimensional empirical mode decomposition (BEMD) technique to decompose the original image into IMFs, then built a video of these IMFs images. The formed video is used as input of 3DCNN model to classify and detect COVID-19 virus. 3DCNN model consists of a 3D VGG-16 backbone followed by a Context-aware attention (CAA) modules then fully connected layers for classification. Each CAA module takes the feature maps of different blocks of the backbone, which allows a learning from different feature maps. In the experiments we used 6484 X-Ray images, 1802 of them were COVID-19 positive cases, 1910 normal cases, and 2772 pneumonia cases. The experiment results showed that our proposed techniques achieved the desired results on the selected dataset. Also, the use of 3DCNN model with contextual information processing exploiting CAA networks helps to achieve better performance.

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